1. Field of the Invention
The present invention relates to component-based object identification systems. More particularly, the present invention relates to training component-based face recognition systems.
2. Description of Background Art
Face recognition techniques generally fall into two categories: global and component-based. In the global approach, one facial image is represented by one feature vector. This feature vector is input into a recognition classifier. The recognition classifier determines the identity of a person based on the feature vector.
In the component-based approach, one facial image is divided into several individual facial components, such as eyes, nose, and mouth. Each facial component is input into a different component recognition classifier. The outputs of the component recognition classifiers are then used to perform face recognition.
Before a component recognition classifier can be used, it should be trained. The better a classifier has been trained, the more accurately it will perform. One way to train a classifier is to present it with a set of examples. Each example is an input-output pair that represents what the classifier should output given a particular input. In other words, the set of examples shown to the classifier determines how accurately the classifier will perform.
As a result, an important characteristic of any component-based object identification system is which components are used as examples to train the system. What is needed is a way to determine which components maximize the system's accuracy in distinguishing a particular object from another.